Evaluating automation bias requires specifically incorporating observation of the clinical decision-making process, for example recording the proportion of doctors who modify AI recommendations, the response time to AI prompts, and the frequency with which erroneous AI outputs are intercepted or let through, rather than focusing only on final outcome metrics.